claude-code-skills-reference/transcript-fixer/references/quick_reference.md

Quick Reference

Storage: transcript-fixer uses SQLite database for corrections storage.

Database location: ~/.transcript-fixer/corrections.db

Quick Start Examples

Adding Corrections via CLI

# Add a simple correction
uv run scripts/fix_transcription.py --add "巨升智能" "具身智能" --domain embodied_ai

# Add corrections for specific domain
uv run scripts/fix_transcription.py --add "奇迹创坛" "奇绩创坛" --domain general
uv run scripts/fix_transcription.py --add "矩阵公司" "初创公司" --domain general

Adding Corrections via SQL

sqlite3 ~/.transcript-fixer/corrections.db

# Insert corrections
INSERT INTO corrections (from_text, to_text, domain, source)
VALUES ('巨升智能', '具身智能', 'embodied_ai', 'manual');

INSERT INTO corrections (from_text, to_text, domain, source)
VALUES ('巨升', '具身', 'embodied_ai', 'manual');

INSERT INTO corrections (from_text, to_text, domain, source)
VALUES ('奇迹创坛', '奇绩创坛', 'general', 'manual');

# Exit
.quit

Adding Context Rules via SQL

Context rules use regex patterns for context-aware corrections:

sqlite3 ~/.transcript-fixer/corrections.db

# Add context-aware rules
INSERT INTO context_rules (pattern, replacement, description, priority)
VALUES ('巨升方向', '具身方向', '巨升→具身', 10);

INSERT INTO context_rules (pattern, replacement, description, priority)
VALUES ('巨升现在', '具身现在', '巨升→具身', 10);

INSERT INTO context_rules (pattern, replacement, description, priority)
VALUES ('近距离的去看', '近距离地去看', '的→地 副词修饰', 5);

# Exit
.quit

Adding Corrections via Python API

Save as add_corrections.py and run with uv run add_corrections.py:

#!/usr/bin/env -S uv run
from pathlib import Path
from core import CorrectionRepository, CorrectionService

# Initialize service
db_path = Path.home() / ".transcript-fixer" / "corrections.db"
repository = CorrectionRepository(db_path)
service = CorrectionService(repository)

# Add corrections
corrections = [
    ("巨升智能", "具身智能", "embodied_ai"),
    ("巨升", "具身", "embodied_ai"),
    ("奇迹创坛", "奇绩创坛", "general"),
    ("火星营", "火星营", "general"),
    ("矩阵公司", "初创公司", "general"),
    ("股价", "框架", "general"),
    ("三观", "三关", "general"),
]

for from_text, to_text, domain in corrections:
    service.add_correction(from_text, to_text, domain)
    print(f"✅ Added: '{from_text}' → '{to_text}' (domain: {domain})")

# Close connection
service.close()

Bulk Import Example

Use the provided bulk import script for importing multiple corrections:

uv run scripts/examples/bulk_import.py

Querying the Database

View Active Corrections

sqlite3 ~/.transcript-fixer/corrections.db "SELECT from_text, to_text, domain FROM active_corrections;"

View Statistics

sqlite3 ~/.transcript-fixer/corrections.db "SELECT * FROM correction_statistics;"

View Context Rules

sqlite3 ~/.transcript-fixer/corrections.db "SELECT pattern, replacement, priority FROM context_rules WHERE is_active = 1 ORDER BY priority DESC;"

See Also

• references/file_formats.md - Complete database schema documentation
• references/script_parameters.md - CLI command reference
• SKILL.md - Main user documentation